Quest enables our customers to transform vast datasets into actionable insights, improve collaboration across the value chain and automate operational processes for optimal efficiency.
Quest supports: digitalisation, operating efficiency, portfolio analysis and optimisation, underwriter decision making, broker reports, risk management, revenue development, product development, client support, predictive pricing, risk scoring, compliance and more.
Collaboration is key to connecting your entire organisation via a single source of truth for your business-critical data. Quest is scalable and connects your organisation with your clients and partners throughout the insurance value chain for maximum efficiency. Reduce administrative burden in the underwriting process by:
By combining your own datasets with other risk-related third-party datasets, and leveraging the latest developments in AI and machine learning you can supercharge your Actuarial team and take control of your book of business.
Using the latest developments in AI and machine learning, Quest is designed to carry out real-time intelligence analysis on your data to identify and highlight risk within your portfolio.
Clear understanding of the factors that influence risk scores and losses can help you to better manage accounts, support clients and take control of loss ratios.
Powerful insights and information relating to underlying risks help Insurers make more informed decisions about the risks they write. A greater understanding of the underlying risk factors can result in Insurers writing risks that they may not have written in the past.
Quest has been designed to scale with your business and to integrate easily with your existing systems for maximum results.
Benefit from our extensive inhouse insurance, data science and technological expertise and get your digital transformation strategy up and running in a matter of weeks. Our team have worked hard to solve real-life problems in collaboration with the insurance community to deliver tangible results.
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* Based on back-testing of models on historical portfolios